The Poisson distribution is a mathematical rule that assigns probabilities to an integer number of occurrences. It has
long been used as an alternative to the binomial distribution when the sample size is large and the occurrence of the
event of interest is rare (Haight, 1967). The Poisson distribution describes a wide range of phenomena in the sciences
such as the number of pollen grains collected in regions of a sticky plate exposed to the open air and the number of
white blood cells found in a cubic centimeter of blood. In many biological, epidemiological, and medical studies, the
comparison of Poisson means (i.e., the average number of occurrences per unit of time or space) from two independent
populations is of great research interest. For instance, in a breast cancer study two groups of women were compared
to determine whether those who had been examined using X-ray fluoroscopy during treatment for tuberculosis had a
higher rate of breast cancer than those who had not been examined using X-ray fluoroscopy (Rothman and Greenland,
1998; Graham et al., 2003). Forty-one cases of breast cancer in 28,010 person-years at risk are reported in the treatment
group with women receiving X-ray fluoroscopy and 15 cases of breast cases in 19,017 person-years at risk in the control
group with women not receiving X-ray fluoroscopy.